For a number of years, researchers have been studying the relationship between the surface electromyogram (EMG) and torque produced about a joint, as a means of non-invasively estimating musculoskeletal load in particular, and the dynamics of joint/musculoskeletal dynamics in general. Measurement and understanding of these dynamics is important in the prevention of musculoskeletal injuries in the workplace (e.g., injuries associated with heavy lifting jobs or repetitive tasks) and in rehabilitation engineering, neuromuscular disease, basic motor control research and other areas. A distinct aspect of load is the "rigidity" that we produce in order to achieve a task. For example, a worker using a power tool (e.g., a hand drill) will purposely co-contract his/her muscles to increase rigidity and stabilize the tool--often without producing any externally measurable torques/forces. However, excessive rigidity (concomitantly producing heavy internal musculoskeletal loads) may be associated with musculoskeletal injury. Currently, no robust methods exist for estimating the degree of rigidity while performing useful tasks. Formally, the mechanical engineering profession more properly defines "rigidity" as the static component of mechanical impedance. For constant-posture tasks (and other limited tasks), mechanical impedance has been measured, but the measurement requires imparting forces on the body, and thus disturbs the task under study. In this grant, we will propose relating mechanical impedance to EMG in a calibration task (in which the body is perturbed), so that after calibration, impedance might be estimated (from EMG) without perturbing the task. This paradigm is identical to EMG-torque modeling. Note that in performing EMG-torque modeling, one usually estimates EMG amplitude (EMGamp) from the EMG waveform, and then develops an EMGamp-torque model. Advanced methods for estimating EMGamp, now available in the literature, have been shown to provide better EMG-torque estimates. Our long-term objectives in this work are to use EMG-based estimates of mechanical impedance to study mechanisms of musculoskeletal injury in occupation tasks, as well as in other applications. Our specific aims are to (a) demonstrate that mechanical impedance about the elbow can be estimated from the EMG in a constant-posture, slowly force-varying task, and (b) demonstrate that advanced methods for estimating EMGamp lead to better EMG-impedance estimates.